This statistic shows the number of honey bee colonies in the United States from 2016 to 2023. In 2023, there were approximately **** million honey bee colonies in the United States, a slight decrease from the previous year.

The number of honey bee colonies in Canada increased by 27.5 thousand numbers (+3.59 percent) in 2023. In total, the number amounted to 794.34 thousand numbers in 2023.

Bumble bees (Bombus) are vitally important pollinators of wild plants and agricultural crops worldwide. Fragmentary observations, however, have suggested population declines in several North American species. Despite rising concern over these observations in the United States, highlighted in a recent National Academy of Sciences report, a national assessment of the geographic scope and possible causal factors of bumble bee decline is lacking. Here, we report results of a 3-y interdisciplinary study of changing distributions, population genetic structure, and levels of pathogen infection in bumble bee populations across the United States. We compare current and historical distributions of eight species, compiling a database of >73,000 museum records for comparison with data from intensive nationwide surveys of >16,000 specimens. We show that the relative abundances of four species have declined by up to 96% and that their surveyed geographic ranges have contracted by 23–87%, some within the last 20 y. We also show that declining populations have significantly higher infection levels of the microsporidian pathogen Nosema bombi and lower genetic diversity compared with co-occurring populations of the stable (nondeclining) species. Higher pathogen prevalence and reduced genetic diversity are, thus, realistic predictors of these alarming patterns of decline in North America, although cause and effect remain uncertain. Bumble bees (Bombus) are integral wild pollinators within native plant communities throughout temperate ecosystems, and recent domestication has boosted their economic importance in crop pollination to a level surpassed only by the honey bee. Their robust size, long tongues, and buzz-pollination behavior (high-frequency buzzing to release pollen from flowers) significantly increase the efficiency of pollen transfer in multibillion dollar crops such as tomatoes and berries. Disturbing reports of bumble bee population declines in Europe have recently spilled over into North America, fueling environmental and economic concerns of global decline. However, the evidence for large-scale range reductions across North America is lacking. Many reports of decline are unpublished, and the few published studies are limited to independent local surveys in northern California/southern Oregon, Ontario, Canada, and Illinois. Furthermore, causal factors leading to the alleged decline of bumble bee populations in North America remain speculative. One compelling but untested hypothesis for the cause of decline in the United States entails the spread of a putatively introduced pathogen, Nosema bombi, which is an obligate intracellular microsporidian parasite found commonly in bumble bees throughout Europe but largely unstudied in North America. Pathogenic effects of N. bombi may vary depending on the host species and reproductive caste and include reductions in colony growth and individual life span and fitness. Population genetic factors could also play a role in Bombus population decline. For instance, small effective population sizes and reduced gene flow among fragmented habitats can result in losses of genetic diversity with negative consequences, and the detrimental impacts of these genetic factors can be especially intensified in bees. Population genetic studies of Bombus are rare worldwide. A single study in the United States identified lower genetic diversity and elevated genetic differentiation (FST) among Illinois populations of the putatively declining B. pensylvanicus relative to those of a codistributed stable species. Similar patterns have been observed in comparative studies of some European species, but most investigations have been geographically restricted and based on limited sampling within and among populations. Although the investigations to date have provided important information on the increasing rarity of some bumble bee species in local populations, the different survey protocols and limited geographic scope of these studies cannot fully capture the general patterns necessary to evaluate the underlying processes or overall gravity of declines. Furthermore, valid tests of the N. bombi hypothesis and its risk to populations across North America call for data on its geographic distribution and infection prevalence among species. Likewise, testing the general importance of population genetic factors in bumble bee decline requires genetic comparisons derived from sampling of multiple stable and declining populations on a large geographic scale. From such range-wide comparisons, we provide incontrovertible evidence that multiple Bombus species have experienced sharp population declines at the national level. We also show that declining populations are associated with both high N. bombi infection levels and low genetic diversity. This data was used in the paper "Patterns of widespread decline in North American bumble bees" published in the Proceedings of the National Academy of United States of America. For more information about this dataset contact: Sydney A. Cameron: scameron@life.illinois.edu James Strange: James.Strange@ars.usda.gov Resources in this dataset:Resource Title: Data from: Patterns of Widespread Decline in North American Bumble Bees (Data Dictionary). File Name: meta.xmlResource Description: This is an XML data dictionary for Data from: Patterns of Widespread Decline in North American Bumble Bees.Resource Title: Patterns of Widespread Decline in North American Bumble Bees (DWC Archive). File Name: occurrence.csvResource Description: File modified to remove fields with no recorded values.Resource Title: Patterns of Widespread Decline in North American Bumble Bees (DWC Archive). File Name: dwca-usda-ars-patternsofwidespreaddecline-bumblebees-v1.1.zipResource Description: Data from: Patterns of Widespread Decline in North American Bumble Bees -- this is a Darwin Core Archive file. The Darwin Core Archive is a zip file that contains three documents.

The occurrence data is stored in the occurrence.txt file. The metadata that describes the columns of this document is called meta.xml. This document is also the data dictionary for this dataset. The metadata that describes the dataset, including author and contact information for this dataset is called eml.xml.

Find the data files at https://bison.usgs.gov/ipt/resource?r=usda-ars-patternsofwidespreaddecline-bumblebees

Bee Colony Census and Loss Data

Bee Colony Census, Survey, and Loss Data in the United States

By Brenda Griffith [source]

About this dataset

The Bee Colony Statistics dataset provides comprehensive data on bee colonies in the United States. It combines information from multiple sources, including the United States Department of Agriculture (USDA) and the Bee Informed Partnership (BIP), to present a detailed overview of bee colony surveys, censuses, and losses.

The USDA data includes three major components. The first is the Bee Colony Survey Data by State, which includes information on various metrics related to beekeeping at a state level. This dataset contains data such as the number of beekeepers exclusive to each state, percentage of colonies managed exclusively in each state, and total winter loss of colonies.

The second component is the Bee Colony Census Data by County, offering insights into specific county-level statistics. It presents a breakdown of colony numbers based on counties and also provides other relevant metrics specific to each county.

Lastly, there is the Bee Colony Census Data by State that expands upon these statistics at a more granular state level perspective. It offers a detailed breakdown of colony numbers for individual states across the country.

Additionally, this dataset incorporates valuable information from BIP—a renowned organization dedicated to studying and improving honeybee health—specifically their Bee Colony Loss data for educational purposes only. The original data ownership remains with BIP.

Important notes regarding this dataset include slight variations between reported losses in publications compared to those shown here due to additional analyses conducted. Losses reported as N/A indicate privacy protection when five or fewer beekeepers responded in a particular state; however, their losses are still included within national statistics.

To delve into more specifics about this dataset's columns: it covers factors such as year, period during which data was collected (e.g., season), geographic location down to county level using ANSI codes for identification, various measured values (e.g., number of colonies), coefficient variation representing relative variability in measurements (%CV), program or survey name from which data originated, week ending date when the data was collected, geographical level at which the data is reported (e.g., state, county), zip code of the location where data belongs, region within the United States, watershed information with corresponding code and name, commodity or product being reported (e.g., honey), specific domain or category to categorize each metric (e.g., loss), value reported for respective columns in numeric format.

Through this dataset compilation and analysis, researchers and beekeepers alike can gain insights into colony health trends and make informed decisions about preserving honeybee populations

How to use the dataset

Here is a step-by-step guide on how to utilize this dataset effectively:

• Understanding the Columns:

  • Year: The year in which the data was collected.
  • Period: The time period during which the data was collected.
  • State: The state in the United States for which the data is reported.
  • State ANSI: The ANSI code for the state.
  • Ag District: The agricultural district within the state for which the data is reported.
  • Ag District Code: The code for the agricultural district.
  • County: The county within the state for which the data is reported.
  • County ANSI: The ANSI code for the county.
  • Value/Total Winter All Loss/Beekeepers/Colonies/CV (%): Different measurements or statistics related to bee colonies and losses.

• Exploration by State: Start by analyzing specific states that are of interest to you. Filter or search based on desired states using their respective column values (e.g., State, State ANSI). This will allow you to focus on a particular region or compare multiple states.

• Investigation by County or Agricultural District: Further narrow your analysis by exploring specific counties or agricultural districts within a state using columns like County, County ANSI, Ag District, and Ag District Code. This can help identify patterns or differences between different areas.

• Understanding Survey Data: Some columns provide information about survey responses from beekeepers such as Beekeepers Exclusive to State (percentage of exclusive beekeepers) and Beekeepers (number of responding beekeepers). These can help gauge the level of participation from beekeepers in different regions.

• ...

Data on the production and value of honey, beekeepers and colonies.

About this dataset

Context

The dataset tabulates the Bee population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Bee across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Bee was 167, a 0.60% decrease year-by-year from 2022. Previously, in 2022, Bee population was 168, a decline of 1.18% compared to a population of 170 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Bee decreased by 56. In this period, the peak population was 223 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Bee is shown in this column.
• Year on Year Change: This column displays the change in Bee population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Bee Population by Year. You can refer the same here

This statistic shows the total number of beehives worldwide from 2010 to 2023. In 2023, there were about *** million beehives worldwide, increasing from around ***** million beehives in the previous year. Number of beehives worldwide has generally been increasing since 2010.

This statistic shows the number of beehives in leading countries worldwide in 2023 (in thousand units). ***** has the largest number of beehives, totaling around **** million, followed by ***** with about *** million.

The honey bee, Apis mellifera L., is one of the main pollinators worldwide. In a temperate climate, seasonality affects the life span, behavior, physiology, and immunity of honey bees. In consequence, it impacts their interaction with pathogens and parasites. In this study, we used Bayesian statistics and modeling to examine the immune response dynamics of summer and winter honey bee workers after injection with the heat-killed bacteria Serratia marcescens, an opportunistic honey bee pathogen. We investigated the humoral and cellular immune response at the transcriptional and functional levels using qPCR of selected immune genes, antimicrobial activity assay, and flow cytometric analysis of hemocyte concentration. Our data demonstrate increased antimicrobial activity at transcriptional and functional levels in summer and winter workers after injection, with a stronger immune response in winter bees. On the other hand, an increase in hemocyte concentration was observed only in the summer bee population. Our results indicate that the summer population mounts a cellular response when challenged with heat-killed S. marcescens, while winter honey bees predominantly rely on humoral immune reactions. We created a model describing the honey bee immune response dynamics to bacteria-derived components by applying Bayesian statistics to our data. This model can be employed in further research and facilitate the investigating of the honey bee immune system and its response to pathogens.

Population genetics statistics for drone fathers from the Arnot Forest, Apiary 1, and Apiary 2, based on alleles from 10 variable microsatellite loci. Drone alleles inferred from worker and queen genotypes using the program COLONY 1.2 [33].Population statistics for drone fathers.

According to a survey conducted in 2020, there were around 96.7 thousand bee colonies in the middle North Island of New Zealand. This region reported the highest bee colony loss in the winter of 2020.

The bee population in Romania totaled nearly 1.92 million bee families in 2022. This represented an increase of approximately 13.5 percent compared to the number of bee colonies registered in 2018.

Population genetic statistics.

Environmental stressors have sublethal consequences on animals, often affecting the mean of phenotypic traits in populations. However, effects on inter-individual variability are poorly understood. Since phenotypic variability is the basis for adaptation, any change due to stressors may have important implications for population resilience. Here we explored this possibility in bees by analysing raw datasets from 23 studies (5,618 bees) in which individuals were first exposed to stressors and then tested for cognitive tasks. While all types of stressors decreased the mean cognitive performance of bees, they increased cognitive variability. Focusing on 14 pesticide studies, we found that the mode of exposure to stressors and the dose were critical. Mean cognitive performance was more affected by a chronic exposure than by an acute exposure. Yet, cognitive variability increased with increasing doses following both exposure durations. Policy implications: Current guidelines for the authorization of plant protection products on the European market prioritize acute over chronic toxicity assessments on non-target organisms. By overlooking the consequences of a chronic exposure, regulatory authorities may register new products or doses that are harmful to bee populations. Our findings call for more research on stress-induced phenotypic variation and its incorporation to policy guidelines to help identify levels and modes of exposure animals can cope with. Methods Search and selection of datasets The search for datasets in scientific publications falling within the scope of our research question was performed in July 2020 using the PubMed database. The words used for the search were (“Stressor” OR “Pesticide” OR “Parasite”) AND (“Cognition” OR “Learning”) AND (“Bees”). This search was not restricted to any section of the manuscripts and automatically extended to similar terms intended under the MeSH hierarchy of the database. A total of 240 studies were found, of which 18 met our inclusion criteria regarding the cognitive task and the type of stressor (see below). The search terms under which each study was found are available in the Supplementary Table 1. Five datasets belonging to the authors of this study were also included as they filled the inclusion criteria. These studies measured the impact of stressors on the cognitive performance of bees. The list of the 23 selected studies is available in Table 1. Cognitive tasks: We focused on cognitive data from bees exposed to stressors during their adult life. The effect of stressors on larvae could not be analysed due to the lack of data available (two studies). In all the selected studies, cognitive performance was assessed using associative learning paradigms testing the ability of bees to associate an olfactory or/and a visual stimulus with an appetitive or aversive reinforcement (Giurfa, 2007). Olfactory learning was tested in 18 out of the 23 studies. These studies used learning protocols based on the appetitive conditioning of the proboscis extension response (PER; 16 studies) or the aversive conditioning of the sting extension response (SER; 2 studies). Either response was conditioned by presenting bees a conditioned stimulus (an odour) reinforced with an unconditioned stimulus (sucrose solution or electric shock), for 3-15 trials in appetitive assays and 5-6 trials in aversive assays. Trainings included absolute learning (the odour is reinforced) and differential learning (an odour is reinforced; the other is not). Visual learning was tested in 5 out of the 23 studies. These studies used appetitive conditioning protocols in a Y-maze or on artificial flowers (i.e. feeders), or aversive conditioning protocols with electric shocks. One of these studies applied a multimodal appetitive conditioning combining both odour and colour cues to be learnt by bees in an array of artificial flowers (Muth et al. 2019). Here again bees were tested for differential learning. Stressors: Stressor types covered different pesticides, parasites, predator odours, alarm pheromones, and heavy metal pollutants. Experiments performed with pesticides whose median lethal dose (LD50; i.e. dose that kills 50% of the population) could not be identified in the literature were excluded from our final selection. Exposure duration: In all the studies, stressors were applied before the cognitive tests, except in one study in which it was used as the conditioning stimulus to be learned (i.e. alarm and predator pheromones (Wang et al. 2016)). We categorised the duration of exposure using the common dichotomy between acute and chronic exposures. An acute exposure was characterized by a single administration of the pesticide to each individual bee. When bees were exposed to the pesticide more than once, either as a substance present in their environment or as a food directly offered to each individual, the exposure type was considered chronic. Bees: The bee species studied in the selected publications were the honey bees Apis cerana and Apis mellifera, and the bumblebees Bombus impatiens and Bombus terrestris. These species were not selected purposefully, but rather emerged as the species most represented in our dataset from the refinement obtained with other inclusion criteria. We considered bee genus (Apis or Bombus) for the analyses.

Table 1: Summary of the 23 studies used.

Stressor

Bee genus

Exposure type

Reference

Pesticide

Apis

Acute

(Ludicke and Nieh, 2020)

Pesticide

Apis

Acute

(Hesselbach and Scheiner, 2018)

Pesticide

Apis

Acute

(Urlacher et al., 2016)

Pesticide

Apis

Acute

(Tan et al., 2015)

Pesticide

Apis

Chronic

(Mustard et al., 2020)

Pesticide

Apis

Chronic

(Tan et al., 2017)

Pesticide

Apis, Bombus

Acute

(Siviter et al., 2019)

Pesticide

Bombus

Acute

(Muth et al., 2019)

Pesticide

Bombus

Acute, chronic

(Stanley, Smith and Raine, 2015)

Pesticide

Bombus

Chronic

(Smith et al., 2020)

Pesticide

Bombus

Chronic

(Lämsä et al., 2018)

Pesticide

Bombus

Chronic

(Phelps et al., 2018)

Pesticide, coexposure

Apis

Chronic

(Colin, Plath, et al., 2020)

Parasite

Bombus

Acute

(Gomez-Moracho et al., 2021)

Parasite

Bombus

Acute

(Martin, Fountain and Brown, 2018)

Pollution

Apis

Acute

(Monchanin et al. unpublished)

Pollution

Apis

Acute

(Monchanin, Drujont, et al., 2021)

Pollution

Apis

Chronic

(Monchanin, Blanc-brude, et al., 2021)

Other

Apis

Acute

(Wang et al., 2016)

Other

Apis

Acute

(Shepherd et al., 2018)

Other

Apis

Chronic

(Shepherd et al., 2019)

Coexposure

Apis, Bombus

Acute/Chronic

(Piiroinen and Goulson, 2016)

Coexposure

Bombus

Acute/Chronic

(Piiroinen et al., 2016)

Dataset organisation and normalisation of variables All but three raw datasets were available online with the published material. Those three datasets were kindly provided by their authors, i.e. Dara Stanley and Ken Tan. The raw data were downloaded and saved as .csv files. A new dataset was created, which combined information on the species, the cognitive task studied, the type of stressor, the type of exposure (acute/chronic), and, in the case of pesticide studies, the dose (µg/bee) or concentration (ppb). The dose (acute exposure) and concentration (chronic exposure) were normalized as the percentage of the LD50. When learning performance was measured as a binary response (e.g. success vs. failure) across multiple trials, the raw data were used to calculate a learning score for each individual corresponding to the number of successful trials. This was required because the variance in binary variables can be mathematically predicted from the mean and sample size and does not reflect biological variance (Supplementary Fig. 1). Each study provided individual cognitive scores for at least one experimental treatment and control group. There was a total of 73 experimental treatments across the 23 studies. To compare the mean cognitive performance and the cognitive variability across studies, we used a standardized method for the meta-analysis of variation (Nakagawa et al., 2015; Senior, Viechtbauer and Nakagawa, 2020). This method controls for the mean – variance linear relationship that may exist in a dataset by using unbiased effect size statistics of the mean and variability, i.e. the natural logarithm of the ratio between the means (lnRR) and the natural logarithm of the ratio between the coefficients of variation (lnCVR) of treated and control groups, respectively. Changes in lnCVR are not an indirect consequence of changes in lnRR, as would have been the case had we analysed the variance and the mean, but they rather reflect changes in variability per se. The two pre-requisites for this method are (i) to use log scale data and (ii) to observe a mean-variance linear relationship. Studies for which negative cognitive scores were present were transformed to log-scale data by adding the minimum score to all individuals. The mean and standard deviation of the cognitive scores, as well as sample sizes, were calculated for each experimental treatment and control group. A linear relationship and positive correlation were found between the log sample mean and standard deviation in our dataset (Supplementary Fig. 2). All pre-requisites being met, we then calculated the lnRR and lnCVR for each experimental treatment and control group (i.e. 73 effect sizes) as well as their sampling (error) variance using equations corrected for the sample size described in (Senior, Viechtbauer and Nakagawa, 2020). Individual bees in control and treated groups in all study designs were considered independent. Data analyses All analyses were conducted in R Studio v.1.2.5033 (RStudio Team 2015). The package metafor (Viechtbauer, 2010)was used to compute multilevel meta-analytic models (MLMA),

The City of Charlotte has been named a Bee City. Pollinator gardens are important in order to maintain the bee population. This map will allow citizens as well as organizations to track the location of pollinator gardens. the intention of this map is to hopefully assist code enforcement so that they don't unintentionally write code violations for a pollinator garden.

Population Estimate, Total, Not Hispanic or Latino, Black or African American Alone (5-year estimate) in Bee County, TX was 1852.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Population Estimate, Total, Not Hispanic or Latino, Black or African American Alone (5-year estimate) in Bee County, TX reached a record high of 3337.00000 in January of 2011 and a record low of 1852.00000 in January of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Not Hispanic or Latino, Black or African American Alone (5-year estimate) in Bee County, TX - last updated from the United States Federal Reserve on June of 2025.

Statistics used to detect recombination in N. apis.

About this dataset

Context

The dataset tabulates the Bee household income by age. The dataset can be utilized to understand the age-based income distribution of Bee income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Bee, NE annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Bee, NE household incomes: Comparative analysis across 16 income brackets

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Bee income distribution by age. You can refer the same here

Additional file 7. Results for the 874 reference samples of HBeeID analyzed using GeneClass2 (GC2). Reference Sets I, II, III and geolocation map (Table S9).

Landscape data was pulled from the USDA-NASS Cropscape database’s 2019 Cropland Data Layer (https://nassgeodata.gmu.edu/CropScape/). A circle with a 2.5-mile (4 km) radius was drawn around each apiary (corresponding to honey bees’ typical foraging range), and land use statistics were calculated within these defined areas of interest. Land use types were sorted into the following categories: grass and pasture, forest and shrubs, water, herbaceous and woody wetlands, developed, corn and soy, and other crops. Proportional land use was calculated by dividing each category’s acreage by the total acreage within the 2.5-mile (4 km) radius. Apiary locations are not disclosed here in order to protect the privacy of the beekeeper who participated in this study. Landscape data for stationary and migratory locations included in this file. (XLSX)